Pandas

df = pd.read_csv("data.csv", index_col="Date", parse_dates=True)

# check if some data is missing (NaN-s)
df.count()

# show df statistic (mean, std etc)
df.describe()

# find rows with NaN-s
df[df.isnull().any(axis=1)]

# remove NaN-s
df1 = df.dropna()

# fill NaN-s
df1 = df.fillna(some_value)
df1 = df.fillna({"foo": 1, "bar": df.bar.mean()})  # column specific

# show unique values (for categorical column)
df.foo.unique()

# show frequency of values
df.foo.value_counts()

# show histogram
%matplotlib inline
df.foo.hist()

# bucketing
pd.cut(df.sentiment, [-1, -0.2, 0.2])

# plot
# kind: "scatter", "bar"
df.plot(kind="scatter", x="foo", y="bar")
df.foo.value_counts().sort_index().plot(kind="bar")

# select multiple columns
pd[["foo", "bar"]]